Investigation of social and psychological factors in late pregnancy among women with perinatal depressive disorder and construction of a predictive model
- VernacularTitle:围产期抑郁障碍产妇妊娠晚期社会心理因素调查分析及预测模型构建
- Author:
Ziyu ZONG
1
;
Fen WANG
Author Information
- Publication Type:Research Article
- Keywords: perinatal period; depressive disorder; late pregnancy; social and psychological factors; predictive model; poor mood
- From: Journal of Clinical Medicine in Practice 2024;28(10):121-125
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate and analyze the social and psychological factors in late pregnancy among women with perinatal depressive disorder (PDD) and construct a predictive model for PDD Methods A total of 88 women diagnosed with PDD were selected as study group, and another 88 healthy women with normal prenatal care were selected as control group in a ratio of 1 to 1. General information and prenatal social and psychological factors related to the two groups were collected. Univariate and multivariate Logistic regression analysis was performed to construct a predictive model for PDD and assess its predictive efficacy. Results Univariate and multivariate Logistic regression analysis showed that gestational age, menstrual mood, history of adverse pregnancy, gender discrimination against women, gender discrimination by parents-in-law, income satisfaction, and depression history in paternal and maternal lineage and three generations were independent influencing factors for PDD in women (
P <0.05). Based on these factors, a predictive model for PDD in women was constructed, the model equation was\begin{document}$P=\frac{1}{1+e-\operatorname{logit}(P)}$\end{document} P )=1.599×gestational age+1.744×poormenstrual mood+0.837×adverse pregnancy history+1.589×gender discrimination against women + 0.820×gender discrimination by parents-in-law+1.089×dissatisfaction with income+2.163×depression history in paternal and maternal lineage and three generations-3.211. The receiver operating characteristic curve showed that the area under the curve for predicting PDD in women using this model was 0.955, with a 95%CI of 0.907 to 0.998, a sensitivity of 0.964, and a specificity of 0.731. The optimal cutoff value was 5.154. Conclusion Gestational age, menstrual mood, adverse pregnancy history, gender discrimination against women, gender discrimination by parents-in-law, income satisfaction, and depression history in paternal and maternal lineage and three generations are independent influencing factors for PDD in women. The predictive model constructed based on these factors has good predictive efficacy for PDD in women, which can contribute to the early prevention and treatment of PDD.
